Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	14.0	5.751719
daal4py_KNeighborsClassifier	0.0	5.0	14.647524
KNeighborsClassifier_kd_tree	0.0	7.0	1.156226
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	58.342032
KMeans_tall	0.0	1.0	46.357864
daal4py_KMeans_tall	0.0	1.0	20.086135
KMeans_short	0.0	0.0	26.516990
daal4py_KMeans_short	0.0	0.0	13.013196
LogisticRegression	0.0	1.0	9.498622
daal4py_LogisticRegression	0.0	1.0	1.267309
Ridge	0.0	1.0	2.979048
daal4py_Ridge	0.0	0.0	23.372518
total	0.0	35.0	43.088614

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.143	0.006	1000000	1000000	100	brute	-1	1	NaN	NaN	0.495	0.008	0.289	0.013	See
1	KNeighborsClassifier	predict	0.184	0.013	1000000	1	100	brute	-1	1	1.0	0.0	0.102	0.004	1.809	0.140	See
2	KNeighborsClassifier	predict	30.775	0.000	1000000	1000	100	brute	-1	1	1.0	0.0	3.842	0.023	8.010	0.047	See
3	KNeighborsClassifier	fit	0.144	0.005	1000000	1000000	100	brute	-1	5	NaN	NaN	0.487	0.004	0.295	0.010	See
4	KNeighborsClassifier	predict	0.195	0.008	1000000	1	100	brute	-1	5	1.0	0.0	0.100	0.003	1.944	0.097	See
5	KNeighborsClassifier	predict	39.682	0.000	1000000	1000	100	brute	-1	5	1.0	0.0	3.854	0.024	10.296	0.065	See
6	KNeighborsClassifier	fit	0.128	0.003	1000000	1000000	100	brute	-1	100	NaN	NaN	0.494	0.010	0.259	0.008	See
7	KNeighborsClassifier	predict	0.189	0.013	1000000	1	100	brute	-1	100	1.0	1.0	0.101	0.002	1.870	0.141	See
8	KNeighborsClassifier	predict	39.844	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	3.917	0.014	10.173	0.037	See
9	KNeighborsClassifier	fit	0.128	0.002	1000000	1000000	100	brute	1	1	NaN	NaN	0.499	0.015	0.256	0.008	See
10	KNeighborsClassifier	predict	0.190	0.003	1000000	1	100	brute	1	1	1.0	0.0	0.102	0.002	1.869	0.051	See
11	KNeighborsClassifier	predict	15.592	0.023	1000000	1000	100	brute	1	1	1.0	0.0	3.850	0.048	4.050	0.051	See
12	KNeighborsClassifier	fit	0.129	0.003	1000000	1000000	100	brute	1	5	NaN	NaN	0.488	0.005	0.264	0.008	See
13	KNeighborsClassifier	predict	0.202	0.005	1000000	1	100	brute	1	5	1.0	0.0	0.099	0.003	2.032	0.078	See
14	KNeighborsClassifier	predict	25.845	0.005	1000000	1000	100	brute	1	5	1.0	0.0	3.864	0.035	6.689	0.061	See
15	KNeighborsClassifier	fit	0.142	0.005	1000000	1000000	100	brute	1	100	NaN	NaN	0.492	0.015	0.288	0.013	See
16	KNeighborsClassifier	predict	0.203	0.006	1000000	1	100	brute	1	100	1.0	1.0	0.101	0.003	2.007	0.089	See
17	KNeighborsClassifier	predict	25.784	0.040	1000000	1000	100	brute	1	100	1.0	1.0	3.917	0.023	6.583	0.040	See
18	KNeighborsClassifier	fit	0.063	0.005	1000000	1000000	2	brute	-1	1	NaN	NaN	0.107	0.004	0.592	0.050	See
19	KNeighborsClassifier	predict	0.020	0.002	1000000	1	2	brute	-1	1	1.0	1.0	0.004	0.000	4.700	0.508	See
20	KNeighborsClassifier	predict	24.904	0.035	1000000	1000	2	brute	-1	1	1.0	1.0	0.833	0.071	29.908	2.545	See
21	KNeighborsClassifier	fit	0.060	0.001	1000000	1000000	2	brute	-1	5	NaN	NaN	0.106	0.004	0.564	0.022	See
22	KNeighborsClassifier	predict	0.028	0.003	1000000	1	2	brute	-1	5	1.0	1.0	0.005	0.000	6.105	0.696	See
23	KNeighborsClassifier	predict	34.340	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.823	0.015	41.750	0.751	See
24	KNeighborsClassifier	fit	0.060	0.002	1000000	1000000	2	brute	-1	100	NaN	NaN	0.105	0.003	0.575	0.024	See
25	KNeighborsClassifier	predict	0.028	0.001	1000000	1	2	brute	-1	100	1.0	1.0	0.005	0.001	5.497	0.604	See
26	KNeighborsClassifier	predict	34.189	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.889	0.009	38.469	0.406	See
27	KNeighborsClassifier	fit	0.060	0.003	1000000	1000000	2	brute	1	1	NaN	NaN	0.107	0.004	0.560	0.035	See
28	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	brute	1	1	1.0	1.0	0.005	0.001	3.300	0.617	See
29	KNeighborsClassifier	predict	10.821	0.058	1000000	1000	2	brute	1	1	1.0	1.0	0.817	0.011	13.251	0.199	See
30	KNeighborsClassifier	fit	0.059	0.002	1000000	1000000	2	brute	1	5	NaN	NaN	0.105	0.002	0.564	0.025	See
31	KNeighborsClassifier	predict	0.022	0.000	1000000	1	2	brute	1	5	1.0	1.0	0.005	0.000	4.794	0.396	See
32	KNeighborsClassifier	predict	20.452	0.040	1000000	1000	2	brute	1	5	1.0	1.0	0.829	0.016	24.670	0.469	See
33	KNeighborsClassifier	fit	0.059	0.002	1000000	1000000	2	brute	1	100	NaN	NaN	0.106	0.004	0.561	0.024	See
34	KNeighborsClassifier	predict	0.022	0.001	1000000	1	2	brute	1	100	1.0	1.0	0.005	0.000	4.694	0.478	See
35	KNeighborsClassifier	predict	20.424	0.065	1000000	1000	2	brute	1	100	1.0	1.0	0.884	0.009	23.091	0.241	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.260	0.029	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.780	0.009	4.182	0.060	See
1	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	6.927	4.104	See
2	KNeighborsClassifier_kd_tree	predict	0.486	0.010	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.113	0.004	4.286	0.165	See
3	KNeighborsClassifier_kd_tree	fit	3.252	0.048	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	1.042	0.018	3.120	0.071	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.001	0.000	6.108	2.652	See
5	KNeighborsClassifier_kd_tree	predict	0.891	0.018	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.227	0.006	3.921	0.132	See
6	KNeighborsClassifier_kd_tree	fit	3.198	0.034	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.817	0.026	3.915	0.133	See
7	KNeighborsClassifier_kd_tree	predict	0.005	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	5.333	2.037	See
8	KNeighborsClassifier_kd_tree	predict	2.825	0.025	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.669	0.007	4.223	0.059	See
9	KNeighborsClassifier_kd_tree	fit	3.240	0.016	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.995	0.074	3.257	0.244	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	3.156	1.386	See
11	KNeighborsClassifier_kd_tree	predict	0.803	0.017	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.126	0.009	6.385	0.498	See
12	KNeighborsClassifier_kd_tree	fit	3.239	0.039	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.815	0.020	3.975	0.110	See
13	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.001	0.000	2.874	1.192	See
14	KNeighborsClassifier_kd_tree	predict	1.550	0.009	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.223	0.008	6.954	0.254	See
15	KNeighborsClassifier_kd_tree	fit	3.256	0.059	1000000	1000000	10	kd_tree	1	100	NaN	NaN	1.000	0.034	3.257	0.126	See
16	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.002	0.003	1.783	2.554	See
17	KNeighborsClassifier_kd_tree	predict	5.163	0.055	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.700	0.016	7.372	0.182	See
18	KNeighborsClassifier_kd_tree	fit	1.315	0.014	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.522	0.015	2.519	0.076	See
19	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	16.274	8.127	See
20	KNeighborsClassifier_kd_tree	predict	0.040	0.003	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	48.114	11.919	See
21	KNeighborsClassifier_kd_tree	fit	1.340	0.010	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.502	0.010	2.670	0.055	See
22	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	13.738	7.045	See
23	KNeighborsClassifier_kd_tree	predict	0.043	0.002	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	34.046	7.068	See
24	KNeighborsClassifier_kd_tree	fit	1.360	0.018	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.511	0.010	2.664	0.063	See
25	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	17.218	8.167	See
26	KNeighborsClassifier_kd_tree	predict	0.063	0.003	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.007	0.001	8.630	0.883	See
27	KNeighborsClassifier_kd_tree	fit	1.335	0.027	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.502	0.007	2.660	0.064	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	4.776	2.443	See
29	KNeighborsClassifier_kd_tree	predict	0.039	0.003	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	47.136	12.201	See
30	KNeighborsClassifier_kd_tree	fit	1.328	0.019	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.524	0.014	2.536	0.078	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	4.967	2.548	See
32	KNeighborsClassifier_kd_tree	predict	0.043	0.003	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	35.096	7.979	See
33	KNeighborsClassifier_kd_tree	fit	1.316	0.015	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.516	0.012	2.552	0.068	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	4.405	2.110	See
35	KNeighborsClassifier_kd_tree	predict	0.072	0.004	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.007	0.001	10.007	0.935	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.647	0.015	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.319	0.008	2.028	0.070	See
1	KMeans_tall	predict	0.001	0.001	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	3.483	4.073	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.790	0.870	See
3	KMeans_tall	fit	0.570	0.013	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.277	0.014	2.057	0.114	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.200	1.157	See
5	KMeans_tall	predict	0.001	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.183	1.407	See
6	KMeans_tall	fit	7.300	0.082	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	3.893	0.027	1.875	0.025	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.924	0.966	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.281	0.901	See
9	KMeans_tall	fit	6.541	0.024	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	3.693	0.018	1.771	0.011	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.658	1.020	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.159	0.940	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.391	0.021	10000	10000	2	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	0.149	0.009	2.626	0.213	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.138	1.563	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.260	0.293	See
3	KMeans_short	fit	0.144	0.003	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.071	0.002	2.036	0.068	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.843	0.989	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.327	0.337	See
6	KMeans_short	fit	1.265	0.035	10000	10000	100	full	k-means++	30	300	1	0.0	22.0	NaN	22.0	NaN	0.599	0.055	2.112	0.202	See
7	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.052	1.016	See
8	KMeans_short	predict	0.007	0.004	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	4.085	2.834	See
9	KMeans_short	fit	0.406	0.062	10000	10000	100	full	random	30	300	1	0.0	30.0	NaN	20.0	NaN	0.314	0.036	1.293	0.247	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.919	0.865	See
11	KMeans_short	predict	0.004	0.003	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	2.444	1.508	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	15.627	0.008	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	15.652	0.023	0.998	0.002	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.377	0.380	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.937	0.343	See
3	LogisticRegression	fit	1.209	0.032	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	1.185	0.027	1.020	0.036	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.000	0.127	0.079	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.005	0.002	0.440	0.182	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	2.741	0.037	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.582	0.018	1.733	0.031	See
1	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.599	0.516	See
2	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.001	0.000	0.681	0.226	See
3	Ridge	fit	1.426	0.036	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.338	0.006	4.225	0.130	See
4	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.434	0.422	See
5	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.597	0.338	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}